library(ggplot2)
setwd("/Users/ab6415/Documents/Work_laptop_stuff/Bioinformatics/Analysis/INTEGRATOR_PAPER_ANALYSES/Fig1_Fig3_FigS3_FigS4_precursors_analysis/length/")
piRNA_counts_wholeanimal_E1 <- list.files(path="../counts/wholeworm/",
pattern="*pis"); piRNA_counts_wholeanimal_E1<-paste0("../counts/wholeworm/",piRNA_counts_wholeanimal_E1,sep="")
piRNA_counts_wholeanimal_E1_longreads <- list.files(path="../counts/wholeworm_longreads/",
pattern="*pis"); piRNA_counts_wholeanimal_E1_longreads<-paste0("../counts/wholeworm_longreads/",piRNA_counts_wholeanimal_E1_longreads,sep="")
piRNA_counts_germnuclei_total <- list.files(path="../counts/germnuclei_total/",
pattern="*pis"); piRNA_counts_germnuclei_total<-paste0("../counts/germnuclei_total/",piRNA_counts_germnuclei_total,sep="")
piRNA_counts_germnuclei_fractionation <- list.files(path="../counts/germnuclei_fractionation_rep1/",
pattern="*pis"); piRNA_counts_germnuclei_fractionation<-paste0("../counts/germnuclei_fractionation_rep1/",piRNA_counts_germnuclei_fractionation,sep="")
piRNA_counts_germnuclei_fractionation_rep2 <- list.files(path="../counts/germnuclei_fractionation_rep2/",
pattern="*pis"); piRNA_counts_germnuclei_fractionation_rep2<-paste0("../counts/germnuclei_fractionation_rep2/",piRNA_counts_germnuclei_fractionation_rep2,sep="")
piRNA_counts_filenames<-c(piRNA_counts_wholeanimal_E1,piRNA_counts_wholeanimal_E1_longreads,piRNA_counts_germnuclei_total,piRNA_counts_germnuclei_fractionation,piRNA_counts_germnuclei_fractionation_rep2)
piRNA_counts_filenames<-piRNA_counts_filenames[c(1:13,19:21,14:18,27:30,22,23,25,26)]
names <-c("wholeworm_EV_col1","wholeworm_ints11_col1","wholeworm_EV_col2","wholeworm_ints11_col2","wholeworm_EV_96h","wholeworm_ints11_96h","wholeworm_EV_col1_longreads","wholeworm_ints11_col1_longreads","wholeworm_ints11_col2_longreads","germnuclei_total_N2_EV","germnuclei_total_N2_ints11","germnuclei_total_TFIIS_EV","germnuclei_total_TFIIS_ints11","germnuclei_NP_N2_EV","germnuclei_NP_N2_ints11","germnuclei_NP_TFIIS_EV","germnuclei_NP_TFIIS_ints11","germnuclei_CHR_N2_EV","germnuclei_CHR_N2_ints11","germnuclei_CHR_TFIIS_EV","germnuclei_CHR_TFIIS_ints11","germnuclei_rep2_NP_N2_EV","germnuclei_rep2_NP_N2_ints11","germnuclei_rep2_NP_TFIIS_EV","germnuclei_rep2_NP_TFIIS_ints11","germnuclei_rep2_CHR_N2_EV","germnuclei_rep2_CHR_N2_ints11","germnuclei_rep2_CHR_TFIIS_EV","germnuclei_rep2_CHR_TFIIS_ints11")
piRNA_counts_filenames
## [1] "../counts/wholeworm/Sample_1_EV_col1.fastq.trimmed.sorted.bed.pis"
## [2] "../counts/wholeworm/Sample_3_ints_11_col1.fastq.trimmed.sorted.bed.pis"
## [3] "../counts/wholeworm/Sample_4_EV_col2.fastq.trimmed.sorted.bed.pis"
## [4] "../counts/wholeworm/Sample_6_ints_11_col2.fastq.trimmed.sorted.bed.pis"
## [5] "../counts/wholeworm/Sample_7_EV_col2_96h.fastq.trimmed.sorted.bed.pis"
## [6] "../counts/wholeworm/Sample_9_ints_11_col2_96h.fastq.trimmed.sorted.bed.pis"
## [7] "../counts/wholeworm_longreads/1_EV_col1_S1_R1_001.fastq.trimmed.sorted.bed.pis"
## [8] "../counts/wholeworm_longreads/3_ints-11_col1_S2_R1_001.fastq.trimmed.sorted.bed.pis"
## [9] "../counts/wholeworm_longreads/9_ints-11_col2_96h_S3_R1_001.fastq.trimmed.sorted.bed.pis"
## [10] "../counts/germnuclei_total/Sample_13_N2_EV_CR_S63_L007_R1_001.fastq.trimmed.sorted.bed.pis"
## [11] "../counts/germnuclei_total/Sample_14_N2_Ints11_CR_S64_L007_R1_001.fastq.trimmed.sorted.bed.pis"
## [12] "../counts/germnuclei_total/Sample_15_TFIIS_EV_CR_S65_L007_R1_001.fastq.trimmed.sorted.bed.pis"
## [13] "../counts/germnuclei_total/Sample_16_TFIIS_Ints11_CR_S66_L007_R1_001.fastq.trimmed.sorted.bed.pis"
## [14] "../counts/germnuclei_fractionation_rep1/7_N2_EV_np_S7_R1_001.fastq.trimmed.sorted.bed.pis"
## [15] "../counts/germnuclei_fractionation_rep1/8_N2_ints-11RNAi_np_S8_R1_001.fastq.trimmed.sorted.bed.pis"
## [16] "../counts/germnuclei_fractionation_rep1/9_TFIIS_EV_np_S9_R1_001.fastq.trimmed.sorted.bed.pis"
## [17] "../counts/germnuclei_fractionation_rep1/10_TFIIS_ints-11RNAi_np_S10_R1_001.fastq.trimmed.sorted.bed.pis"
## [18] "../counts/germnuclei_fractionation_rep1/25_N2_EV_chr_S11_R1_001.fastq.trimmed.sorted.bed.pis"
## [19] "../counts/germnuclei_fractionation_rep1/26_N2_ints-11RNAi_chr_S12_R1_001.fastq.trimmed.sorted.bed.pis"
## [20] "../counts/germnuclei_fractionation_rep1/27_TFIIS_EV_chr_S13_R1_001.fastq.trimmed.sorted.bed.pis"
## [21] "../counts/germnuclei_fractionation_rep1/28_TFIIS_ints-11RNAi_chr_S14_R1_001.fastq.trimmed.sorted.bed.pis"
## [22] "../counts/germnuclei_fractionation_rep2/5_N2_EV_np_S5_R1_001.fastq.trimmed.sorted.bed.pis"
## [23] "../counts/germnuclei_fractionation_rep2/6_N2_ints11RNAi_np_S6_R1_001.fastq.trimmed.sorted.bed.pis"
## [24] "../counts/germnuclei_fractionation_rep2/7_TFIIS_EV_np_S7_R1_001.fastq.trimmed.sorted.bed.pis"
## [25] "../counts/germnuclei_fractionation_rep2/8_TFIIS_ints11RNAi_np_S8_R1_001.fastq.trimmed.sorted.bed.pis"
## [26] "../counts/germnuclei_fractionation_rep2/1_N2_EV_chr_S1_R1_001.fastq.trimmed.sorted.bed.pis"
## [27] "../counts/germnuclei_fractionation_rep2/13_N2_ints11RNAi_chr_techrep_S9_R1_001.fastq.trimmed.sorted.bed.pis"
## [28] "../counts/germnuclei_fractionation_rep2/3_TFIIS_EV_chr_S3_R1_001.fastq.trimmed.sorted.bed.pis"
## [29] "../counts/germnuclei_fractionation_rep2/4_TFIIS_ints11RNAi_chr_S4_R1_001.fastq.trimmed.sorted.bed.pis"
names
## [1] "wholeworm_EV_col1" "wholeworm_ints11_col1"
## [3] "wholeworm_EV_col2" "wholeworm_ints11_col2"
## [5] "wholeworm_EV_96h" "wholeworm_ints11_96h"
## [7] "wholeworm_EV_col1_longreads" "wholeworm_ints11_col1_longreads"
## [9] "wholeworm_ints11_col2_longreads" "germnuclei_total_N2_EV"
## [11] "germnuclei_total_N2_ints11" "germnuclei_total_TFIIS_EV"
## [13] "germnuclei_total_TFIIS_ints11" "germnuclei_NP_N2_EV"
## [15] "germnuclei_NP_N2_ints11" "germnuclei_NP_TFIIS_EV"
## [17] "germnuclei_NP_TFIIS_ints11" "germnuclei_CHR_N2_EV"
## [19] "germnuclei_CHR_N2_ints11" "germnuclei_CHR_TFIIS_EV"
## [21] "germnuclei_CHR_TFIIS_ints11" "germnuclei_rep2_NP_N2_EV"
## [23] "germnuclei_rep2_NP_N2_ints11" "germnuclei_rep2_NP_TFIIS_EV"
## [25] "germnuclei_rep2_NP_TFIIS_ints11" "germnuclei_rep2_CHR_N2_EV"
## [27] "germnuclei_rep2_CHR_N2_ints11" "germnuclei_rep2_CHR_TFIIS_EV"
## [29] "germnuclei_rep2_CHR_TFIIS_ints11"
piRNA_counts <- lapply(piRNA_counts_filenames,function(i){
read.csv(paste(i,sep=""), header=FALSE,sep="\t")
})
piRNA_counts_precfiltered <- lapply(piRNA_counts, function(df){
df$plus_d <- df$V10-df$V2
df$minus_d <- df$V3-df$V11
df_plus<-df[which(df$plus_d==2 & df$V8=="+"),]
df_minus<-df[which(df$minus_d==(2) & df$V8=="-"),]
return(rbind(df_plus,df_minus))
})
motifdep_piRNA_precursors<-lapply(piRNA_counts_precfiltered,function(df){return(df[c(grep("^21UR-",df$V12),grep("^piRNA_type_1",df$V12)),])})
motifind_piRNA_precursors<-lapply(piRNA_counts_precfiltered,function(df){return(df[grep("^piRNA_type_2",df$V12),])})
N2_EV_np<-rbind(motifdep_piRNA_precursors[[14]],motifdep_piRNA_precursors[[22]])
N2_EV_np<-aggregate(N2_EV_np[,"V6"],by=N2_EV_np[c("V1","V2","V3","V4","V5","V12")],FUN=sum)
N2_ints11_np<-rbind(motifdep_piRNA_precursors[[15]],motifdep_piRNA_precursors[[23]])
N2_ints11_np<-aggregate(N2_ints11_np[,"V6"],by=N2_ints11_np[c("V1","V2","V3","V4","V5","V12")],FUN=sum)
tfiis_EV_np<-rbind(motifdep_piRNA_precursors[[16]],motifdep_piRNA_precursors[[24]])
tfiis_EV_np<-aggregate(tfiis_EV_np[,"V6"],by=tfiis_EV_np[c("V1","V2","V3","V4","V5","V12")],FUN=sum)
tfiis_ints11_np<-rbind(motifdep_piRNA_precursors[[17]],motifdep_piRNA_precursors[[25]])
tfiis_ints11_np<-aggregate(tfiis_ints11_np[,"V6"],by=tfiis_ints11_np[c("V1","V2","V3","V4","V5","V12")],FUN=sum)
N2_EV_chr<-rbind(motifdep_piRNA_precursors[[18]],motifdep_piRNA_precursors[[26]])
N2_EV_chr<-aggregate(N2_EV_chr[,"V6"],by=N2_EV_chr[c("V1","V2","V3","V4","V5","V12")],FUN=sum)
N2_ints11_chr<-rbind(motifdep_piRNA_precursors[[19]],motifdep_piRNA_precursors[[27]])
N2_ints11_chr<-aggregate(N2_ints11_chr[,"V6"],by=N2_ints11_chr[c("V1","V2","V3","V4","V5","V12")],FUN=sum)
tfiis_EV_chr<-rbind(motifdep_piRNA_precursors[[20]],motifdep_piRNA_precursors[[28]])
tfiis_EV_chr<-aggregate(tfiis_EV_chr[,"V6"],by=tfiis_EV_chr[c("V1","V2","V3","V4","V5","V12")],FUN=sum)
tfiis_ints11_chr<-rbind(motifdep_piRNA_precursors[[21]],motifdep_piRNA_precursors[[29]])
tfiis_ints11_chr<-aggregate(tfiis_ints11_chr[,"V6"],by=tfiis_ints11_chr[c("V1","V2","V3","V4","V5","V12")],FUN=sum)
motifdep_piRNA_precursors_agg<-list(N2_EV_np,N2_ints11_np,tfiis_EV_np,tfiis_ints11_np,
N2_EV_chr,N2_ints11_chr,tfiis_EV_chr,tfiis_ints11_chr)
names_agg<-c("N2_EV_np","N2_ints11_np","tfiis_EV_np","tfiis_ints11_np",
"N2_EV_chr","N2_ints11_chr","tfiis_EV_chr","tfiis_ints11_chr")
Locus-by-locus analysis
Median length shift
library(reshape2)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
motifdep_piRNA_precursors_data_summary<-lapply(motifdep_piRNA_precursors_agg,function(df){
number_seqs<-aggregate(df[,"V12"],by=list(df[,"V12"]),FUN=length)
median_length<-aggregate(df[,"V4"],by=list(df[,"V12"]),FUN=median)
mean_length<-aggregate(df[,"V4"],by=list(df[,"V12"]),FUN=mean)
number_reads<-aggregate(df[,"x"],by=list(df[,"V12"]),FUN=sum)
df_uncol<-df[rep(row.names(df), df$x),]
median_length_numreads<-aggregate(df_uncol[,"V4"],by=list(df_uncol[,"V12"]),FUN=median)
mean_length_numreads<-aggregate(df_uncol[,"V4"],by=list(df_uncol[,"V12"]),FUN=mean)
merged<-data.frame(number_seqs$x,
median_length$x,
mean_length$x,
number_reads$x,
median_length_numreads$x,
mean_length_numreads$x)
colnames(merged)<-c("number_seqs","median_length","mean_length","number_reads","median_length_numreads","mean_length_numreads")
rownames(merged)<-number_seqs$Group.1
return(merged)
})
merged_df<-motifdep_piRNA_precursors_data_summary[[1]]
colnames(merged_df)<-paste0(names_agg[1],colnames(merged_df))
for (i in seq(2,length(names_agg))){
df<-motifdep_piRNA_precursors_data_summary[[i]]
colnames(df)<-paste0(names_agg[i],colnames(df))
merged_df<-merge(merged_df,df,by=0,all=TRUE)
rownames(merged_df)<-merged_df$Row.names
merged_df$Row.names<-NULL
}
merged_df$rownames<-rownames(merged_df)
sample_index_to_medianlength_index<-function(index){return((index-1)*6+2)}
lenshift<-function(df,treatment_index,control_index,numseqs_threshold){
index1<-sample_index_to_medianlength_index(treatment_index)
index2<-sample_index_to_medianlength_index(control_index)
df<-df[which(df[,index1-1]>numseqs_threshold & df[,index2-1]>numseqs_threshold),]
out<-data.frame(names=as.character(df$rownames),lenshift=df[,index1]-df[,index2])
out$names<-as.character(out$names)
return(out)
}
test<-merged_df[sample_index_to_medianlength_index(2)]-merged_df[sample_index_to_medianlength_index(1)]
test<-na.exclude(test$N2_ints11_npmedian_length)
cor(test,lenshift(merged_df,2,1,0)[,2])
## [1] 1
thr<-0
median_lenshift_thr<-data.frame(
names<-c(
lenshift(merged_df,2,1,thr)[,1],
lenshift(merged_df,3,1,thr)[,1],
lenshift(merged_df,4,1,thr)[,1],
lenshift(merged_df,6,5,thr)[,1],
lenshift(merged_df,7,5,thr)[,1],
lenshift(merged_df,8,5,thr)[,1]),
median_length_shift<-c(
lenshift(merged_df,2,1,thr)[,2],
lenshift(merged_df,3,1,thr)[,2],
lenshift(merged_df,4,1,thr)[,2],
lenshift(merged_df,6,5,thr)[,2],
lenshift(merged_df,7,5,thr)[,2],
lenshift(merged_df,8,5,thr)[,2]),
sample<-c(
rep(names_agg[2],nrow(lenshift(merged_df,2,1,thr))),
rep(names_agg[3],nrow(lenshift(merged_df,3,1,thr))),
rep(names_agg[4],nrow(lenshift(merged_df,4,1,thr))),
rep(names_agg[6],nrow(lenshift(merged_df,6,5,thr))),
rep(names_agg[7],nrow(lenshift(merged_df,7,5,thr))),
rep(names_agg[8],nrow(lenshift(merged_df,8,5,thr)))
),
fraction<-c(
rep("nucleoplasm",nrow(lenshift(merged_df,2,1,thr))),
rep("nucleoplasm",nrow(lenshift(merged_df,3,1,thr))),
rep("nucleoplasm",nrow(lenshift(merged_df,4,1,thr))),
rep("chromatin",nrow(lenshift(merged_df,6,5,thr))),
rep("chromatin",nrow(lenshift(merged_df,7,5,thr))),
rep("chromatin",nrow(lenshift(merged_df,8,5,thr)))
),
condition<-c(
rep("N2 ints-11 RNAi",nrow(lenshift(merged_df,2,1,thr))),
rep("tfiis EV",nrow(lenshift(merged_df,3,1,thr))),
rep("tfiis ints-11 RNAi",nrow(lenshift(merged_df,4,1,thr))),
rep("N2 ints-11 RNAi",nrow(lenshift(merged_df,6,5,thr))),
rep("tfiis EV",nrow(lenshift(merged_df,7,5,thr))),
rep("tfiis ints-11 RNAi",nrow(lenshift(merged_df,8,5,thr)))
))
colnames(median_lenshift_thr)<-c("piRNA_IDs","median length shift","sample","fraction","condition")
median_lenshift_thr$sample<-factor(median_lenshift_thr$sample,levels = names_agg)
median_lenshift_thr$condition<-factor(median_lenshift_thr$condition,levels=c("N2 ints-11 RNAi","tfiis EV","tfiis ints-11 RNAi"))
median_lenshift_thr$fraction<-factor(median_lenshift_thr$fraction,levels=c("total","nucleoplasm","chromatin"))
ggplot(median_lenshift_thr)+geom_boxplot(aes(y=`median length shift`,x=sample,col=fraction,fill=condition),outlier.shape=NA)+
coord_cartesian(ylim=c(-25,50))+
scale_fill_manual(values=c(alpha("green",0.2),alpha("red",0.2),alpha("blue",0.2)))+
scale_colour_manual(values=c("red","black"))+
geom_hline(yintercept = 0,col="red",linetype="dashed")+
theme_classic()+
ylab("median length shift")+
ggtitle("median length shift - collapsed sequences")
ggsave("median_length_shift_collapsedreads_aggreps.pdf")
## Saving 7 x 5 in image
pausing_strength_data<-read.table("../../Reference_pausing_signal_strength/all_pis_typeI_pausingscores.bed",header=TRUE)
pausing_strength_data<-pausing_strength_data[,c(4,10:12)]
pausing_strength_data$V4<-as.character(pausing_strength_data$V4)
median_lenshift_thr_pausingstrength<-merge(median_lenshift_thr,pausing_strength_data,
by.x="piRNA_IDs",by.y="V4")
median_lenshift_thr_pausingstrength$tm_downstream_bins<-factor(median_lenshift_thr_pausingstrength$tm_downstream_bins, levels=c("(-713,-94.8]","(-94.8,-18.3]","(-18.3,39.8]","(39.8,101]","(101,412]"))
table(median_lenshift_thr_pausingstrength$tm_downstream_bins)
##
## (-713,-94.8] (-94.8,-18.3] (-18.3,39.8] (39.8,101] (101,412]
## 2117 2017 1929 1921 1530
ggplot(median_lenshift_thr_pausingstrength)+geom_boxplot(aes(y=`median length shift`,x=sample,col=fraction,fill=tm_downstream_bins),outlier.shape=NA)+
coord_cartesian(ylim=c(-25,50))+
scale_colour_manual(values=c("red","black"))+
scale_fill_brewer(palette = "GnBu")+
geom_hline(yintercept = 0,col="red",linetype="dashed")+
theme_classic()+
ylab("median length shift")+
ggtitle("median length shift - collapsed sequences")
ggsave("median_length_shift_collapsedreads_aggreps_pausingstrength.pdf")
## Saving 7 x 5 in image
Mean length shift
sample_index_to_meanlength_index<-function(index){return((index-1)*6+3)}
lenshift<-function(df,treatment_index,control_index,numseqs_threshold){
index1<-sample_index_to_meanlength_index(treatment_index)
index2<-sample_index_to_meanlength_index(control_index)
df<-df[which(df[,index1-1]>numseqs_threshold & df[,index2-1]>numseqs_threshold),]
out<-data.frame(names=as.character(df$rownames),lenshift=df[,index1]-df[,index2])
out$names<-as.character(out$names)
return(out)
}
thr<-0
thr<-0
median_lenshift_thr<-data.frame(
names<-c(
lenshift(merged_df,2,1,thr)[,1],
lenshift(merged_df,3,1,thr)[,1],
lenshift(merged_df,4,1,thr)[,1],
lenshift(merged_df,6,5,thr)[,1],
lenshift(merged_df,7,5,thr)[,1],
lenshift(merged_df,8,5,thr)[,1]),
median_length_shift<-c(
lenshift(merged_df,2,1,thr)[,2],
lenshift(merged_df,3,1,thr)[,2],
lenshift(merged_df,4,1,thr)[,2],
lenshift(merged_df,6,5,thr)[,2],
lenshift(merged_df,7,5,thr)[,2],
lenshift(merged_df,8,5,thr)[,2]),
sample<-c(
rep(names_agg[2],nrow(lenshift(merged_df,2,1,thr))),
rep(names_agg[3],nrow(lenshift(merged_df,3,1,thr))),
rep(names_agg[4],nrow(lenshift(merged_df,4,1,thr))),
rep(names_agg[6],nrow(lenshift(merged_df,6,5,thr))),
rep(names_agg[7],nrow(lenshift(merged_df,7,5,thr))),
rep(names_agg[8],nrow(lenshift(merged_df,8,5,thr)))
),
fraction<-c(
rep("nucleoplasm",nrow(lenshift(merged_df,2,1,thr))),
rep("nucleoplasm",nrow(lenshift(merged_df,3,1,thr))),
rep("nucleoplasm",nrow(lenshift(merged_df,4,1,thr))),
rep("chromatin",nrow(lenshift(merged_df,6,5,thr))),
rep("chromatin",nrow(lenshift(merged_df,7,5,thr))),
rep("chromatin",nrow(lenshift(merged_df,8,5,thr)))
),
condition<-c(
rep("N2 ints-11 RNAi",nrow(lenshift(merged_df,2,1,thr))),
rep("tfiis EV",nrow(lenshift(merged_df,3,1,thr))),
rep("tfiis ints-11 RNAi",nrow(lenshift(merged_df,4,1,thr))),
rep("N2 ints-11 RNAi",nrow(lenshift(merged_df,6,5,thr))),
rep("tfiis EV",nrow(lenshift(merged_df,7,5,thr))),
rep("tfiis ints-11 RNAi",nrow(lenshift(merged_df,8,5,thr)))
))
colnames(median_lenshift_thr)<-c("piRNA_IDs","mean length shift","sample","fraction","condition")
median_lenshift_thr$sample<-factor(median_lenshift_thr$sample,levels = names_agg)
median_lenshift_thr$condition<-factor(median_lenshift_thr$condition,levels=c("N2 ints-11 RNAi","tfiis EV","tfiis ints-11 RNAi"))
median_lenshift_thr$fraction<-factor(median_lenshift_thr$fraction,levels=c("total","nucleoplasm","chromatin"))
ggplot(median_lenshift_thr)+geom_boxplot(aes(y=`mean length shift`,x=sample,col=fraction,fill=condition),outlier.shape=NA)+
coord_cartesian(ylim=c(-25,50))+
scale_fill_manual(values=c(alpha("green",0.2),alpha("red",0.2),alpha("blue",0.2)))+
scale_colour_manual(values=c("red","black"))+
geom_hline(yintercept = 0,col="red",linetype="dashed")+
theme_classic()+
ylab("mean length shift")+
ggtitle("mean length shift - collapsed sequences")
ggsave("mean_length_shift_collapsedreads_aggreps.pdf")
## Saving 7 x 5 in image
median_lenshift_thr_pausingstrength<-merge(median_lenshift_thr,pausing_strength_data,
by.x="piRNA_IDs",by.y="V4")
median_lenshift_thr_pausingstrength$tm_downstream_bins<-factor(median_lenshift_thr_pausingstrength$tm_downstream_bins, levels=c("(-713,-94.8]","(-94.8,-18.3]","(-18.3,39.8]","(39.8,101]","(101,412]"))
table(median_lenshift_thr_pausingstrength$tm_downstream_bins)
##
## (-713,-94.8] (-94.8,-18.3] (-18.3,39.8] (39.8,101] (101,412]
## 2117 2017 1929 1921 1530
ggplot(median_lenshift_thr_pausingstrength)+geom_boxplot(aes(y=`mean length shift`,x=sample,col=fraction,fill=tm_downstream_bins),outlier.shape=NA)+
coord_cartesian(ylim=c(-25,50))+
scale_colour_manual(values=c("red","black"))+
scale_fill_brewer(palette = "GnBu")+
geom_hline(yintercept = 0,col="red",linetype="dashed")+
theme_classic()+
ylab("mean length shift")+
ggtitle("mean length shift - collapsed sequences")
ggsave("mean_length_shift_collapsedreads_aggreps_pausingstrength.pdf")
## Saving 7 x 5 in image
#ints11 alone in NP
index1<-sample_index_to_meanlength_index(1)
index2<-sample_index_to_meanlength_index(2)
wilcox.test(merged_df[,index1],merged_df[,index2],paired = TRUE)
##
## Wilcoxon signed rank test with continuity correction
##
## data: merged_df[, index1] and merged_df[, index2]
## V = 184710, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
#tfiis in NP
index1<-sample_index_to_meanlength_index(1)
index2<-sample_index_to_meanlength_index(3)
wilcox.test(merged_df[,index1],merged_df[,index2],paired = TRUE)
##
## Wilcoxon signed rank test with continuity correction
##
## data: merged_df[, index1] and merged_df[, index2]
## V = 179596, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
#ints11 tfiis in NP
index1<-sample_index_to_meanlength_index(1)
index2<-sample_index_to_meanlength_index(4)
wilcox.test(merged_df[,index1],merged_df[,index2],paired = TRUE)
##
## Wilcoxon signed rank test with continuity correction
##
## data: merged_df[, index1] and merged_df[, index2]
## V = 60802, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
#ints11 alone in CHR
index1<-sample_index_to_meanlength_index(5)
index2<-sample_index_to_meanlength_index(6)
wilcox.test(merged_df[,index1],merged_df[,index2],paired = TRUE)
##
## Wilcoxon signed rank test with continuity correction
##
## data: merged_df[, index1] and merged_df[, index2]
## V = 518957, p-value = 2.804e-10
## alternative hypothesis: true location shift is not equal to 0
#tfiis in CHR
index1<-sample_index_to_meanlength_index(5)
index2<-sample_index_to_meanlength_index(7)
wilcox.test(merged_df[,index1],merged_df[,index2],paired = TRUE)
##
## Wilcoxon signed rank test with continuity correction
##
## data: merged_df[, index1] and merged_df[, index2]
## V = 1113636, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
#ints11 tfiis in CHR
index1<-sample_index_to_meanlength_index(5)
index2<-sample_index_to_meanlength_index(8)
wilcox.test(merged_df[,index1],merged_df[,index2],paired = TRUE)
##
## Wilcoxon signed rank test with continuity correction
##
## data: merged_df[, index1] and merged_df[, index2]
## V = 284494, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
Median length shift
sample_index_to_medianlengthNumreads_index<-function(index){return((index-1)*6+5)}
lenshift<-function(df,treatment_index,control_index,numseqs_threshold){
index1<-sample_index_to_medianlengthNumreads_index(treatment_index)
index2<-sample_index_to_medianlengthNumreads_index(control_index)
df<-df[which(df[,index1-1]>numseqs_threshold & df[,index2-1]>numseqs_threshold),]
out<-data.frame(names=as.character(df$rownames),lenshift=df[,index1]-df[,index2])
out$names<-as.character(out$names)
return(out)
}
thr<-0
median_lenshift_thr<-data.frame(
names<-c(
lenshift(merged_df,2,1,thr)[,1],
lenshift(merged_df,3,1,thr)[,1],
lenshift(merged_df,4,1,thr)[,1],
lenshift(merged_df,6,5,thr)[,1],
lenshift(merged_df,7,5,thr)[,1],
lenshift(merged_df,8,5,thr)[,1]),
median_length_shift<-c(
lenshift(merged_df,2,1,thr)[,2],
lenshift(merged_df,3,1,thr)[,2],
lenshift(merged_df,4,1,thr)[,2],
lenshift(merged_df,6,5,thr)[,2],
lenshift(merged_df,7,5,thr)[,2],
lenshift(merged_df,8,5,thr)[,2]),
sample<-c(
rep(names_agg[2],nrow(lenshift(merged_df,2,1,thr))),
rep(names_agg[3],nrow(lenshift(merged_df,3,1,thr))),
rep(names_agg[4],nrow(lenshift(merged_df,4,1,thr))),
rep(names_agg[6],nrow(lenshift(merged_df,6,5,thr))),
rep(names_agg[7],nrow(lenshift(merged_df,7,5,thr))),
rep(names_agg[8],nrow(lenshift(merged_df,8,5,thr)))
),
fraction<-c(
rep("nucleoplasm",nrow(lenshift(merged_df,2,1,thr))),
rep("nucleoplasm",nrow(lenshift(merged_df,3,1,thr))),
rep("nucleoplasm",nrow(lenshift(merged_df,4,1,thr))),
rep("chromatin",nrow(lenshift(merged_df,6,5,thr))),
rep("chromatin",nrow(lenshift(merged_df,7,5,thr))),
rep("chromatin",nrow(lenshift(merged_df,8,5,thr)))
),
condition<-c(
rep("N2 ints-11 RNAi",nrow(lenshift(merged_df,2,1,thr))),
rep("tfiis EV",nrow(lenshift(merged_df,3,1,thr))),
rep("tfiis ints-11 RNAi",nrow(lenshift(merged_df,4,1,thr))),
rep("N2 ints-11 RNAi",nrow(lenshift(merged_df,6,5,thr))),
rep("tfiis EV",nrow(lenshift(merged_df,7,5,thr))),
rep("tfiis ints-11 RNAi",nrow(lenshift(merged_df,8,5,thr)))
))
colnames(median_lenshift_thr)<-c("piRNA_IDs","median length shift","sample","fraction","condition")
median_lenshift_thr$sample<-factor(median_lenshift_thr$sample,levels = names_agg)
median_lenshift_thr$condition<-factor(median_lenshift_thr$condition,levels=c("N2 ints-11 RNAi","tfiis EV","tfiis ints-11 RNAi"))
median_lenshift_thr$fraction<-factor(median_lenshift_thr$fraction,levels=c("total","nucleoplasm","chromatin"))
ggplot(median_lenshift_thr)+geom_boxplot(aes(y=`median length shift`,x=sample,col=fraction,fill=condition),outlier.shape=NA)+
coord_cartesian(ylim=c(-25,50))+
scale_fill_manual(values=c(alpha("green",0.2),alpha("red",0.2),alpha("blue",0.2)))+
scale_colour_manual(values=c("red","black"))+
geom_hline(yintercept = 0,col="red",linetype="dashed")+
theme_classic()+
ylab("median length shift")+
ggtitle("median length shift - uncollapsed reads")
ggsave("median_length_shift_uncollapsedreads_aggreps.pdf")
## Saving 7 x 5 in image
median_lenshift_thr_pausingstrength<-merge(median_lenshift_thr,pausing_strength_data,
by.x="piRNA_IDs",by.y="V4")
median_lenshift_thr_pausingstrength$tm_downstream_bins<-factor(median_lenshift_thr_pausingstrength$tm_downstream_bins, levels=c("(-713,-94.8]","(-94.8,-18.3]","(-18.3,39.8]","(39.8,101]","(101,412]"))
table(median_lenshift_thr_pausingstrength$tm_downstream_bins)
##
## (-713,-94.8] (-94.8,-18.3] (-18.3,39.8] (39.8,101] (101,412]
## 2117 2017 1929 1921 1530
ggplot(median_lenshift_thr_pausingstrength)+geom_boxplot(aes(y=`median length shift`,x=sample,col=fraction,fill=tm_downstream_bins),outlier.shape=NA)+
coord_cartesian(ylim=c(-25,50))+
scale_colour_manual(values=c("red","black"))+
scale_fill_brewer(palette = "GnBu")+
geom_hline(yintercept = 0,col="red",linetype="dashed")+
theme_classic()+
ylab("median length shift")+
ggtitle("median length shift - uncollapsed reads")
ggsave("median_length_shift_uncollapsedreads_aggreps_pausingstrength.pdf")
## Saving 7 x 5 in image
Mean length shift - uncollapsed reads
sample_index_to_meanlengthNumreads_index<-function(index){return((index-1)*6+6)}
lenshift<-function(df,treatment_index,control_index,numseqs_threshold){
index1<-sample_index_to_medianlengthNumreads_index(treatment_index)
index2<-sample_index_to_medianlengthNumreads_index(control_index)
df<-df[which(df[,index1-1]>numseqs_threshold & df[,index2-1]>numseqs_threshold),]
out<-data.frame(names=as.character(df$rownames),lenshift=df[,index1]-df[,index2])
out$names<-as.character(out$names)
return(out)
}
thr<-0
median_lenshift_thr<-data.frame(
names<-c(
lenshift(merged_df,2,1,thr)[,1],
lenshift(merged_df,3,1,thr)[,1],
lenshift(merged_df,4,1,thr)[,1],
lenshift(merged_df,6,5,thr)[,1],
lenshift(merged_df,7,5,thr)[,1],
lenshift(merged_df,8,5,thr)[,1]),
median_length_shift<-c(
lenshift(merged_df,2,1,thr)[,2],
lenshift(merged_df,3,1,thr)[,2],
lenshift(merged_df,4,1,thr)[,2],
lenshift(merged_df,6,5,thr)[,2],
lenshift(merged_df,7,5,thr)[,2],
lenshift(merged_df,8,5,thr)[,2]),
sample<-c(
rep(names_agg[2],nrow(lenshift(merged_df,2,1,thr))),
rep(names_agg[3],nrow(lenshift(merged_df,3,1,thr))),
rep(names_agg[4],nrow(lenshift(merged_df,4,1,thr))),
rep(names_agg[6],nrow(lenshift(merged_df,6,5,thr))),
rep(names_agg[7],nrow(lenshift(merged_df,7,5,thr))),
rep(names_agg[8],nrow(lenshift(merged_df,8,5,thr)))
),
fraction<-c(
rep("nucleoplasm",nrow(lenshift(merged_df,2,1,thr))),
rep("nucleoplasm",nrow(lenshift(merged_df,3,1,thr))),
rep("nucleoplasm",nrow(lenshift(merged_df,4,1,thr))),
rep("chromatin",nrow(lenshift(merged_df,6,5,thr))),
rep("chromatin",nrow(lenshift(merged_df,7,5,thr))),
rep("chromatin",nrow(lenshift(merged_df,8,5,thr)))
),
condition<-c(
rep("N2 ints-11 RNAi",nrow(lenshift(merged_df,2,1,thr))),
rep("tfiis EV",nrow(lenshift(merged_df,3,1,thr))),
rep("tfiis ints-11 RNAi",nrow(lenshift(merged_df,4,1,thr))),
rep("N2 ints-11 RNAi",nrow(lenshift(merged_df,6,5,thr))),
rep("tfiis EV",nrow(lenshift(merged_df,7,5,thr))),
rep("tfiis ints-11 RNAi",nrow(lenshift(merged_df,8,5,thr)))
))
colnames(median_lenshift_thr)<-c("piRNA_IDs","mean length shift","sample","fraction","condition")
median_lenshift_thr$sample<-factor(median_lenshift_thr$sample,levels = names_agg)
median_lenshift_thr$condition<-factor(median_lenshift_thr$condition,levels=c("N2 ints-11 RNAi","tfiis EV","tfiis ints-11 RNAi"))
median_lenshift_thr$fraction<-factor(median_lenshift_thr$fraction,levels=c("total","nucleoplasm","chromatin"))
ggplot(median_lenshift_thr)+geom_boxplot(aes(y=`mean length shift`,x=sample,col=fraction,fill=condition),outlier.shape=NA)+
coord_cartesian(ylim=c(-25,50))+
scale_fill_manual(values=c(alpha("green",0.2),alpha("red",0.2),alpha("blue",0.2)))+
scale_colour_manual(values=c("red","black"))+
geom_hline(yintercept = 0,col="red",linetype="dashed")+
theme_classic()+
ylab("mean length shift")+
ggtitle("mean length shift - uncollapsed reads")
ggsave("mean_length_shift_uncollapsedreads_aggreps.pdf")
## Saving 7 x 5 in image
median_lenshift_thr_pausingstrength<-merge(median_lenshift_thr,pausing_strength_data,
by.x="piRNA_IDs",by.y="V4")
median_lenshift_thr_pausingstrength$tm_downstream_bins<-factor(median_lenshift_thr_pausingstrength$tm_downstream_bins, levels=c("(-713,-94.8]","(-94.8,-18.3]","(-18.3,39.8]","(39.8,101]","(101,412]"))
table(median_lenshift_thr_pausingstrength$tm_downstream_bins)
##
## (-713,-94.8] (-94.8,-18.3] (-18.3,39.8] (39.8,101] (101,412]
## 2117 2017 1929 1921 1530
ggplot(median_lenshift_thr_pausingstrength)+geom_boxplot(aes(y=`mean length shift`,x=sample,col=fraction,fill=tm_downstream_bins),outlier.shape=NA)+
coord_cartesian(ylim=c(-25,50))+
scale_colour_manual(values=c("red","black"))+
scale_fill_brewer(palette = "GnBu")+
geom_hline(yintercept = 0,col="red",linetype="dashed")+
theme_classic()+
ylab("mean length shift")+
ggtitle("mean length shift - uncollapsed reads")
ggsave("mean_length_shift_uncollapsedreads_aggreps_pausingstrength.pdf")
## Saving 7 x 5 in image
#ints11 alone in NP
index1<-sample_index_to_meanlengthNumreads_index(1)
index2<-sample_index_to_meanlengthNumreads_index(2)
wilcox.test(merged_df[,index1],merged_df[,index2],paired = TRUE)
##
## Wilcoxon signed rank test with continuity correction
##
## data: merged_df[, index1] and merged_df[, index2]
## V = 194502, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
#tfiis in NP
index1<-sample_index_to_meanlengthNumreads_index(1)
index2<-sample_index_to_meanlengthNumreads_index(3)
wilcox.test(merged_df[,index1],merged_df[,index2],paired = TRUE)
##
## Wilcoxon signed rank test with continuity correction
##
## data: merged_df[, index1] and merged_df[, index2]
## V = 186895, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
#ints11 tfiis in NP
index1<-sample_index_to_meanlengthNumreads_index(1)
index2<-sample_index_to_meanlengthNumreads_index(4)
wilcox.test(merged_df[,index1],merged_df[,index2],paired = TRUE)
##
## Wilcoxon signed rank test with continuity correction
##
## data: merged_df[, index1] and merged_df[, index2]
## V = 61056, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
#ints11 alone in CHR
index1<-sample_index_to_meanlengthNumreads_index(5)
index2<-sample_index_to_meanlengthNumreads_index(6)
wilcox.test(merged_df[,index1],merged_df[,index2],paired = TRUE)
##
## Wilcoxon signed rank test with continuity correction
##
## data: merged_df[, index1] and merged_df[, index2]
## V = 467499, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
#tfiis in CHR
index1<-sample_index_to_meanlengthNumreads_index(5)
index2<-sample_index_to_meanlengthNumreads_index(7)
wilcox.test(merged_df[,index1],merged_df[,index2],paired = TRUE)
##
## Wilcoxon signed rank test with continuity correction
##
## data: merged_df[, index1] and merged_df[, index2]
## V = 1053894, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
#ints11 tfiis in CHR
index1<-sample_index_to_meanlengthNumreads_index(5)
index2<-sample_index_to_meanlengthNumreads_index(8)
wilcox.test(merged_df[,index1],merged_df[,index2],paired = TRUE)
##
## Wilcoxon signed rank test with continuity correction
##
## data: merged_df[, index1] and merged_df[, index2]
## V = 277252, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
Median length heatmap and correlations
merged_df_medianlength<-merged_df[,seq(2,44,by = 6)]
colnames(merged_df_medianlength)<-names_agg
merged_df_medianlength<-merged_df_medianlength[order(apply(merged_df_medianlength,MARGIN=1,FUN=sum,na.rm=TRUE),
decreasing=FALSE),]
merged_df_medianlength$id<-rownames(merged_df_medianlength)
melted_df<-melt(merged_df_medianlength)
## Using id as id variables
melted_df$id<-factor(melted_df$id,levels=merged_df_medianlength$id)
ggplot(melted_df, aes(variable,id, fill=value)) + geom_raster() +
theme(axis.text.y=element_blank(),
axis.ticks.y=element_blank())+
scale_fill_gradientn(colors = c("blue","pink","red"))+
ylab("motif-dependent piRNA loci")+xlab("condition")
cor(merged_df_medianlength[,1:8], use = "pairwise.complete.obs")
## N2_EV_np N2_ints11_np tfiis_EV_np tfiis_ints11_np N2_EV_chr
## N2_EV_np 1.0000000 0.3660523 0.4106685 0.2646039 0.3558378
## N2_ints11_np 0.3660523 1.0000000 0.4582556 0.5076408 0.3725152
## tfiis_EV_np 0.4106685 0.4582556 1.0000000 0.4021936 0.4746218
## tfiis_ints11_np 0.2646039 0.5076408 0.4021936 1.0000000 0.3870915
## N2_EV_chr 0.3558378 0.3725152 0.4746218 0.3870915 1.0000000
## N2_ints11_chr 0.2840870 0.3640658 0.3181022 0.4378607 0.4740678
## tfiis_EV_chr 0.3054843 0.4103333 0.5112650 0.4152849 0.5844457
## tfiis_ints11_chr 0.1672338 0.2898436 0.3796898 0.4070145 0.4064303
## N2_ints11_chr tfiis_EV_chr tfiis_ints11_chr
## N2_EV_np 0.2840870 0.3054843 0.1672338
## N2_ints11_np 0.3640658 0.4103333 0.2898436
## tfiis_EV_np 0.3181022 0.5112650 0.3796898
## tfiis_ints11_np 0.4378607 0.4152849 0.4070145
## N2_EV_chr 0.4740678 0.5844457 0.4064303
## N2_ints11_chr 1.0000000 0.4116580 0.3955375
## tfiis_EV_chr 0.4116580 1.0000000 0.4512372
## tfiis_ints11_chr 0.3955375 0.4512372 1.0000000
ggplot(merged_df_medianlength)+geom_point(aes(x=N2_EV_np,y=N2_EV_chr))
## Warning: Removed 5094 rows containing missing values (geom_point).
ggplot(merged_df_medianlength)+geom_point(aes(x=N2_ints11_np,y=N2_ints11_chr))
## Warning: Removed 5895 rows containing missing values (geom_point).
ggplot(merged_df_medianlength)+geom_point(aes(x=tfiis_EV_np,y=tfiis_EV_chr))
## Warning: Removed 5441 rows containing missing values (geom_point).
ggplot(merged_df_medianlength)+geom_point(aes(x=tfiis_ints11_np,y=N2_ints11_chr))
## Warning: Removed 6085 rows containing missing values (geom_point).
merged_df_medianlength_pausingstrength<-merge(merged_df_medianlength,pausing_strength_data,
by.x=0,by.y="V4")
merged_df_medianlength_pausingstrength$tm_downstream_bins<-factor(merged_df_medianlength_pausingstrength$tm_downstream_bins,
levels = c("(-713,-94.8]","(-94.8,-18.3]","(-18.3,39.8]",
"(39.8,101]","(101,412]"))
ggplot(merged_df_medianlength_pausingstrength)+geom_point(aes(x=N2_EV_np,y=N2_EV_chr,col=tm_downstream_bins),
size=0.5,stroke=0.5,alpha=0.5)+
facet_grid(. ~ tm_downstream_bins)+theme_classic()+theme(aspect.ratio = 1)
## Warning: Removed 5094 rows containing missing values (geom_point).
ggsave("np_vs_chr_medianlength_N2_EV.pdf")
## Saving 7 x 5 in image
## Warning: Removed 5094 rows containing missing values (geom_point).
ggplot(merged_df_medianlength_pausingstrength)+geom_point(aes(x=N2_ints11_np,y=N2_ints11_chr,col=tm_downstream_bins),
size=0.5,stroke=0.5,alpha=0.5)+
facet_grid(. ~ tm_downstream_bins)+theme_classic()+theme(aspect.ratio = 1)
## Warning: Removed 5895 rows containing missing values (geom_point).
ggsave("np_vs_chr_medianlength_N2_ints11RNAi.pdf")
## Saving 7 x 5 in image
## Warning: Removed 5895 rows containing missing values (geom_point).
ggplot(merged_df_medianlength_pausingstrength)+geom_point(aes(x=tfiis_EV_np,y=tfiis_EV_chr,col=tm_downstream_bins),
size=0.5,stroke=0.5,alpha=0.5)+
facet_grid(. ~ tm_downstream_bins)+theme_classic()+theme(aspect.ratio = 1)
## Warning: Removed 5441 rows containing missing values (geom_point).
ggsave("np_vs_chr_medianlength_tfiis_EV.pdf")
## Saving 7 x 5 in image
## Warning: Removed 5441 rows containing missing values (geom_point).
ggplot(merged_df_medianlength_pausingstrength)+geom_point(aes(x=tfiis_ints11_np,y=tfiis_ints11_chr,col=tm_downstream_bins),
size=0.5,stroke=0.5,alpha=0.5)+
facet_grid(. ~ tm_downstream_bins)+theme_classic()+theme(aspect.ratio = 1)
## Warning: Removed 5806 rows containing missing values (geom_point).
ggsave("np_vs_chr_medianlength_tfiis_ints11RNAi.pdf")
## Saving 7 x 5 in image
## Warning: Removed 5806 rows containing missing values (geom_point).
Mean length heatmap and correlations
merged_df_meanlength<-merged_df[,seq(3,45,by = 6)]
colnames(merged_df_meanlength)<-names_agg
merged_df_meanlength<-merged_df_meanlength[order(apply(merged_df_meanlength,MARGIN=1,FUN=sum,na.rm=TRUE),
decreasing=FALSE),]
merged_df_meanlength$id<-rownames(merged_df_meanlength)
melted_df<-melt(merged_df_meanlength)
## Using id as id variables
melted_df$id<-factor(melted_df$id,levels=merged_df_meanlength$id)
ggplot(melted_df, aes(variable,id, fill=value)) + geom_raster() +
theme(axis.text.y=element_blank(),
axis.ticks.y=element_blank())+
scale_fill_gradientn(colors = c("blue","pink","red"))+
ylab("motif-dependent piRNA loci")+xlab("condition")
cor(merged_df_meanlength[,1:8], use = "pairwise.complete.obs")
## N2_EV_np N2_ints11_np tfiis_EV_np tfiis_ints11_np N2_EV_chr
## N2_EV_np 1.0000000 0.3756494 0.4151220 0.2727414 0.3784628
## N2_ints11_np 0.3756494 1.0000000 0.4540211 0.5258158 0.3846776
## tfiis_EV_np 0.4151220 0.4540211 1.0000000 0.4002352 0.4712512
## tfiis_ints11_np 0.2727414 0.5258158 0.4002352 1.0000000 0.3808146
## N2_EV_chr 0.3784628 0.3846776 0.4712512 0.3808146 1.0000000
## N2_ints11_chr 0.3034803 0.3624034 0.3085957 0.4439244 0.4639374
## tfiis_EV_chr 0.3291791 0.4237846 0.5066498 0.4174960 0.5973806
## tfiis_ints11_chr 0.1885830 0.2966646 0.3699152 0.4099851 0.4102228
## N2_ints11_chr tfiis_EV_chr tfiis_ints11_chr
## N2_EV_np 0.3034803 0.3291791 0.1885830
## N2_ints11_np 0.3624034 0.4237846 0.2966646
## tfiis_EV_np 0.3085957 0.5066498 0.3699152
## tfiis_ints11_np 0.4439244 0.4174960 0.4099851
## N2_EV_chr 0.4639374 0.5973806 0.4102228
## N2_ints11_chr 1.0000000 0.4031160 0.3980147
## tfiis_EV_chr 0.4031160 1.0000000 0.4447199
## tfiis_ints11_chr 0.3980147 0.4447199 1.0000000
ggplot(merged_df_meanlength)+geom_point(aes(x=N2_EV_np,y=N2_EV_chr))+xlim(18,75)
## Warning: Removed 5094 rows containing missing values (geom_point).
ggplot(merged_df_meanlength)+geom_point(aes(x=N2_ints11_np,y=N2_ints11_chr))+xlim(18,75)
## Warning: Removed 5895 rows containing missing values (geom_point).
ggplot(merged_df_meanlength)+geom_point(aes(x=tfiis_EV_np,y=tfiis_EV_chr))+xlim(18,75)
## Warning: Removed 5441 rows containing missing values (geom_point).
ggplot(merged_df_meanlength)+geom_point(aes(x=tfiis_ints11_np,y=N2_ints11_chr))+xlim(18,75)
## Warning: Removed 6085 rows containing missing values (geom_point).
merged_df_meanlength_pausingstrength<-merge(merged_df_meanlength,pausing_strength_data,
by.x=0,by.y="V4")
merged_df_meanlength_pausingstrength$tm_downstream_bins<-factor(merged_df_meanlength_pausingstrength$tm_downstream_bins,
levels = c("(-713,-94.8]","(-94.8,-18.3]","(-18.3,39.8]",
"(39.8,101]","(101,412]"))
ggplot(merged_df_meanlength_pausingstrength)+geom_point(aes(x=N2_EV_np,y=N2_EV_chr,col=tm_downstream_bins),
size=0.5,stroke=0.5,alpha=0.5)+
facet_grid(. ~ tm_downstream_bins)+theme_classic()+theme(aspect.ratio = 1)
## Warning: Removed 5094 rows containing missing values (geom_point).
ggsave("np_vs_chr_meanlength_N2_EV.pdf")
## Saving 7 x 5 in image
## Warning: Removed 5094 rows containing missing values (geom_point).
ggplot(merged_df_meanlength_pausingstrength)+geom_point(aes(x=N2_ints11_np,y=N2_ints11_chr,col=tm_downstream_bins),
size=0.5,stroke=0.5,alpha=0.5)+
facet_grid(. ~ tm_downstream_bins)+theme_classic()+theme(aspect.ratio = 1)
## Warning: Removed 5895 rows containing missing values (geom_point).
ggsave("np_vs_chr_meanlength_N2_ints11RNAi.pdf")
## Saving 7 x 5 in image
## Warning: Removed 5895 rows containing missing values (geom_point).
ggplot(merged_df_meanlength_pausingstrength)+geom_point(aes(x=tfiis_EV_np,y=tfiis_EV_chr,col=tm_downstream_bins),
size=0.5,stroke=0.5,alpha=0.5)+
facet_grid(. ~ tm_downstream_bins)+theme_classic()+theme(aspect.ratio = 1)
## Warning: Removed 5441 rows containing missing values (geom_point).
ggsave("np_vs_chr_meanlength_tfiis_EV.pdf")
## Saving 7 x 5 in image
## Warning: Removed 5441 rows containing missing values (geom_point).
ggplot(merged_df_meanlength_pausingstrength)+geom_point(aes(x=tfiis_ints11_np,y=tfiis_ints11_chr,col=tm_downstream_bins),
size=0.5,stroke=0.5,alpha=0.5)+
facet_grid(. ~ tm_downstream_bins)+theme_classic()+theme(aspect.ratio = 1)
## Warning: Removed 5806 rows containing missing values (geom_point).
ggsave("np_vs_chr_meanlength_tfiis_ints11RNAi.pdf")
## Saving 7 x 5 in image
## Warning: Removed 5806 rows containing missing values (geom_point).